Aircraft mask monitoring system

ABSTRACT

An aircraft mask monitoring system is provided, which includes a plurality of masks respectively corresponding to a plurality of seats in an aircraft. The masks are configured to deliver oxygen. Each mask includes at least one of a plurality of sensors, and each sensor is configured to detect an operational parameter of the respective mask as sensor data. The system further includes one or more processors configured to communicate with the sensors via a wired or wireless connection, receive the sensor data from the sensors, store the sensor data in an associated storage device, and process the sensor data using anomaly detection logic to generate a sensor data report. The sensor data report includes an anomaly state of at least one mask. The one or more processors are further configured to output the sensor data report for display on a client computing device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 63/201,480, filed Apr. 30, 2021, the entirety of which ishereby incorporated herein by reference for all purposes.

FIELD

The present disclosure relates generally to aircraft, and moreparticularly to the monitoring of oxygen delivery systems.

BACKGROUND

Most aircraft include oxygen delivery systems for passengers and crew touse during emergency events, for example, when the cabin pressurizationfails. These systems include masks for delivering oxygen directly toindividual people. Passenger masks in particular are relatively simple,typically consisting of a truncated conical cup to be placed on thepassenger's face, an elastic band to secure the cup in place, a tubeconnecting the mask to an oxygen source, and a reservoir bag. Passengersare instructed in how to use the masks, and individuals unable toproperly use a mask during an emergency event due to improper use or amedical emergency may end up relying on fellow passengers or crew tovisually notice the issue, and therefore may be overlooked.

In addition, maintaining such systems to ensure proper deployment duringan emergency event can be complex, involving overhaul, testing,inspection, repair, replacement, recertification, scheduling time formaintenance, and accommodation of Aircraft-on-Ground events. It may bemore cost effective to simply discard and replace many components on aperiodic schedule based on a predicted lifespan, or after each emergencyevent, rather than test each individual component. This can result inunnecessary replacements and the associated costs for implementing thereplacements, or faulty equipment not being discovered in advance ofscheduled maintenance.

SUMMARY

To address the above issues, according to one aspect of the presentdisclosure, an aircraft mask monitoring system is provided herein. Inthis aspect, the aircraft mask monitoring system includes a plurality ofmasks respectively corresponding to a plurality of seats in an aircraft,wherein the masks are configured to deliver oxygen. The aircraft maskmonitoring system includes a plurality of sensors, wherein each of theplurality of masks includes at least one of the plurality of sensors,and wherein each sensor is configured to detect an operational parameterof the respective mask as sensor data. The aircraft mask monitoringsystem further includes one or more processors configured to communicatewith the plurality of sensors via a wired or wireless connection,receive the sensor data from the plurality of sensors, store the sensordata in an associated storage device, process the sensor data usinganomaly detection logic to generate a sensor data report, wherein thesensor data report includes an anomaly state of at least one mask fromthe plurality of masks, and output the sensor data report for display ona client computing device.

Another aspect of the present disclosure relates to a method ofoperating an aircraft mask monitoring system, the system comprising aplurality of masks respectively corresponding to a plurality of seats inan aircraft, and the masks configured to deliver oxygen. In this aspect,the method includes providing each of the plurality of masks with atleast one of a plurality of sensors, detecting, as sensor data, at leastone operational parameter of a mask from the plurality of masks, andreceiving the sensor data from the plurality of sensors at one or moreprocessors. The method further includes storing the sensor data in anassociated storage device, processing the sensor data using anomalydetection logic to generate a sensor data report, wherein the sensordata report includes an anomaly state of at least one mask from theplurality of masks, and outputting the sensor data report for display ona client computing device.

Still another aspect of the present disclosure relates to a mask foroxygen delivery in an aircraft. In this aspect, the mask includes a maskbody configured to cover a nose and mouth of a passenger, a bagconnected to the mask body and to an oxygen source, and a plurality ofsensors configured to detect a plurality of operational parameters ofthe mask as sensor data. The plurality of sensors include a carbondioxide sensor, a mask state sensor, an oxygen flow sensor, atemperature sensor, and a humidity sensor. The plurality of sensors areconfigured to communicate the sensor data to one or more processors viaa wired or wireless connection.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an aircraft mask monitoring system applied to an emergencyoxygen supply system onboard an aircraft.

FIG. 2 shows the aircraft mask monitoring system of FIG. 1.

FIG. 3 shows a flight deck view of a dashboard presented by the systemof FIG. 1.

FIG. 4 shows a cabin crew view of a dashboard presented by the system ofFIG. 1.

FIG. 5 shows an operations center view of a dashboard presented by thesystem of FIG. 1.

FIG. 6 shows a detailed view of the operations center view of FIG. 5.

FIG. 7 shows a maintenance view of a dashboard presented by the systemof FIG. 1.

FIG. 8 shows a method of operating an aircraft mask monitoring system.

FIG. 9 shows a schematic view of an example computing system.

DETAILED DESCRIPTION

FIG. 1 shows an aircraft mask monitoring system 100 applied to anemergency oxygen supply system 1 onboard an aircraft 2. The emergencyoxygen supply system 1 includes an oxygen source 3 such as an oxygentank or an oxygen generator and tubing 4 for supplying oxygen from theoxygen source 3 to different locations throughout the aircraft 2. Theemergency oxygen supply system 1 can further include hoses 5 connectingvarious masks to the emergency oxygen supply system 1. For example, theaircraft mask monitoring system 100 includes a plurality of masks 10respectively corresponding to a plurality of seats 6 (only one isillustrated for simplicity) in the aircraft 2. The masks 10 areconfigured to deliver oxygen to passengers, crew, pilots, etc. In someimplementations, the masks 10 are a type provided to passengers in apassenger cabin of the aircraft 2.

FIG. 2 shows the aircraft mask monitoring system 100 of FIG. 1. One ofthe masks 10 is illustrated by way of example. In one implementation,the mask 10 is for oxygen delivery in the aircraft 2 and includes a maskbody 12 configured to cover a nose and mouth of a passenger 7, and a bag14 connected to the mask body 12 and to an oxygen source 3. An elasticstrap 13 may secure the mask body 12 over the passenger's face. Theaircraft mask monitoring system 100 includes a plurality of sensors 16,and each of the plurality of masks 10 includes at least one of theplurality of sensors 16. Each sensor 16 is configured to detect anoperational parameter of the respective mask 10 as sensor data 18. Forexample, the plurality of sensors 16 can include one or more of a carbondioxide sensor, a mask state sensor, an oxygen flow sensor, atemperature sensor, a humidity sensor, an oxygen tank level sensor,and/or an oxygen generator sensor, among others. The oxygen tank levelsensor and the oxygen generator sensor can be included depending on thetype of oxygen source 3 used in the emergency oxygen supply system 1 andcan detect the remaining amount of oxygen left to deliver from theoxygen source 3.

When included, the mask state sensor can detect a state of therespective mask 10, such as “Online,” “Offline,” “Deployed,” “In use,”“Maintenance mode,” and “Unknown.” For example, “Online” can indicatethat a mask 10 is ready to be deployed and in working order, “Offline”can indicate that a mask 10 is known to be unavailable to be deployed,“Deployed” can indicate that a mask 10 has been ejected from its storagecompartment but is not yet in use, “In use” can indicate that a personis wearing and breathing through a mask, “Maintenance mode” can indicatethat the mask is deliberately set into a mode for performing maintenancein which sensor data 18 can be collected in a different manner thanduring normal operation so as to avoid producing erroneous data, and“Unknown” can indicate an error state. Other suitable statuses may beavailable. The mask state sensor can include any one or combination ofindividual sensors such as a touch sensor, an optical sensor, a triggerswitch, proximity sensor, etc. The mask state sensor can also be a softsensor utilizing any combination of the previously discussed sensortypes to determine the mask state. Together, the plurality of sensors 16can be used to monitor the status, condition, and use of the mask 10, aswell as the status of the person 7 wearing the mask 10.

The aircraft mask monitoring system 100 includes a hub computing device20 and any number of client computing devices 22, each including arespective processor 24, 26. Aspects of the aircraft mask monitoringsystem 100 can be performed by separate computing devices or acombination thereof. The client computing devices 22 can includesmartphones, tablets, laptops, and desktop computers, for example. Theaircraft mask monitoring system 100 includes one or more processors(e.g., processors 24, 26) configured to communicate with the pluralityof sensors 16 via a wired or wireless connection. A wired connectionsuch as by using a cable 28 may reduce a charging and batteryreplacement burden for the mask 10. A wireless connection may reduce aweight of the mask 10 and can be implemented, for example, via radiotransceivers. The one or more processors are configured to receive thesensor data 18 from the plurality of sensors 16. In some situations, thesensor data can be first received by a local access point 30 and thenforwarded to the hub computing device 20 or a client computing device 22over a wired or wireless connection. The wireless connection can be, forexample, a mesh network, a Wireless Local Area Network, or short-rangeradio. In one implementation, the sensor data 18 is temporarily storedonboard and then forwarded to the hub computing device 20 offsite. Inthis case, the data can be stored locally and streamed in real time, oralternatively offloaded after the aircraft lands. To reduce the amountof data transferred, the sensor data 18 can be filtered and/orcompressed, for example, by reporting only data of particular concern.Data may be further reduced by setting the sampling rate of theplurality of sensors 16 lower in times of inactivity and increasing thesampling rate after a triggering event.

The one or more processors are configured to store the sensor data 18 inan associated storage device (e.g., storage device 32 of the hubcomputing device 20 or storage device 34 of the client computing device22). The one or more processors are configured to process the sensordata 18 using anomaly detection logic 36 to generate a sensor datareport 38. The sensor data report 38 includes an anomaly state of atleast one mask 10 from the plurality of masks 10. Finally, the one ormore processors are configured to output the sensor data report 38 fordisplay on the client computing device 22. Examples of the sensor datareport are described below with reference to FIGS. 3-8. The sensor datareport 38 can be used for a variety of purposes such as monitoringpassenger or crew health, directing resources during an emergency, andproviding a basis for a customized maintenance schedule.

The sensor data 18 can be stored in the form of time series data, forexample. Accordingly, the anomaly detection logic 36 can include, but isnot limited to, a time series-based anomaly or outlier detectionalgorithm using Density Based Spatial Clustering of Applications withNoise (DBSCAN) using a rolling window based DBSCAN, Holt-Winters (i.e.,Triple Exponential Smooth), or Auto-Regressive Integrated Moving Average(ARIMA).

In some implementations, the one or more processors 24, 26 can befurther configured to output a flight deck view 40 (see FIG. 3) or acabin crew view 42 (see FIG. 4) on the client computing device 22 inwhich the sensor data report 38 includes a status of each of theplurality of masks 10, a data stream 44 of the sensor data 18 over agiven time period, and a metric 46 indicating passenger health. Theflight deck view 40 can be intended to be operated by the pilot,co-pilot, or anyone else in the flight deck of the aircraft 2, while thecabin crew view 42 can be intended to be operated by the cabin crewonboard. These views 40, 42 can allow the aircraft personnel to accessthe aircraft mask monitoring system 100 and send and receive alerts in atimely fashion. Any of the provided views can be presented on a deviceinstalled in the aircraft 2, a desktop computer, a laptop computer, amobile device such as a tablet or smartphone, etc.

The status is shown by way of example in a chart 48 indicating theproportion of masks 10 of each status by using different colors, andalso shown in greater detail in a status detail section 50 below thechart 48. Other methods of displaying the status may be used. Inaddition, any of the views presented by the client computing device 22can include an aircraft map 52 where the status of each mask 10, oxygensource 3, etc., monitored by the plurality of sensors 16 may beindicated by color, shape, numerical value, etc. These views 40, 42 canbe used by the flight deck and the cabin crew, respectively, to monitorthe status of the oxygen delivery system and the plurality of masks atany time, and of the passengers on board during an emergency in whichthe plurality of masks 10 are deployed. The data stream 44 can displaythe sensor data 18 as a compilation or average of all or a subset ofmasks 10, or a single selected mask 10 may be shown.

The metric 46 can provide a simple visual cue of the overall passengerhealth, the health of a selected subset of passengers, or a specificpassenger health if one mask 10 is selected. The metric 46 isillustrated by way of example as a percentage but may take any suitableform, and is determined based at least on the sensor data 18 from therelevant masks 10. The metric 46 can indicate how many passengers are orare not wearing and using a mask 10, or can indicate the overall statusof the passengers who are wearing a mask 10. For example, a low oxygenor carbon dioxide flow rate may indicate that a passenger is not gettingenough oxygen or is not breathing well, or the mask state could showthat the passenger is not wearing the mask correctly, any of which maybe considered an example of an anomaly state. The anomaly state caninclude, for example, at least one of an offline state, an unknownstate, a malfunction or depleted state of an associated oxygen tank, amalfunction or depleted state of an oxygen generator, an improperlydeployed state, an abnormal breathing state of a person wearing the mask10, and/or a medical emergency state of the person wearing the mask 10.One oxygen source 3 may deliver oxygen to one or more passengers, andtherefore a malfunction or low oxygen supply indicated by the sensordata 18 may affect the health and safety of the relevant passenger(s). Alow metric 46 below a predefined threshold value can trigger a presetaction such as alerting the crew or emergency responders, or suchactions may be triggered by sensor data 18 from any individual orcombination of sensors 16 reaching a corresponding threshold value.

As discussed above, the status of each mask 10 and oxygen source 3 maybe indicated in the aircraft map 52. In the depicted example, the statusis indicated by color and letter. Individual masks 10 can be selected orsubset may be selected by status via checkboxes 54. Using the flightdeck view 40, a user can select one of several graphical user interface(GUI) objects to perform a desired action. Many different actions areprogrammable and the actions are not limited to those shown or discussedbelow. For example, when one or more masks 10 are selected, the user mayselect an object 56A to direct the cabin crew to perform a cabin checkfor the corresponding passengers, or select an object 56B in order toreset the sensors 16 of the selected mask(s) 10. As more examples, theuser may select an object 56C to notify the operations center locatedremotely from the aircraft 2 of an issue with one or more of the oxygensources 3, or an object 56D to notify the cabin crew to stand ready forpassenger assistance or further instructions. Additional options mayinclude, for example, an object 56E to issue a cabin check to allpassengers, an object 56F to send the operations center a mask alert, anobject 56G for setting and sending a custom notification, an object 56Hfor selecting which sensor data 18 to display in the data stream 44, andone or more objects relating to models used to process the sensor data18, as discussed below. In some implementations, a simplified version ofthe flight deck view 40 as well as the views of FIGS. 4-8 are presentedwhen an emergency event has not occurred, with less data and feweroptions presented to the user.

The flight deck view 40 can include an object 56I that is selectable tocreate a forecast of where the sensor data 18 will be within a certaintime, e.g., 45 minutes to an hour, using data analytics forecastingmethods. The user can select the object 56I to activate an“auto-forecast” option for a specified period of time which will providea simulation according to the current sensor data 18 and referencemodels that the aircraft mask monitoring system 100 has been trained on.Alternatively, the user can select specific models such as certain timeseries models like ARIMA or GARCH by selecting the object 56J. An object56K can be selected to activate mathematical solvers, and an object 56Lcan be selected to display a predictive model of what might happen withthe sensor data 18 in the future.

The one or more processors 24, 26 can be further configured to execute adecision support model 58 (see FIG. 2) that is trained to receive thesensor data 18 and output a recommended mask action 60 in view of theanomaly state, such as alerting the user, the crew, the operationscenter, or emergency responders that the passenger wearing the mask 10requires assistance. The decision support model 58 can be a prescriptivemodel that runs in an automatic mode, or is activated by selecting anobject 56M. For example, in the cabin crew view 42 of FIG. 4, one mask10 is highlighted on the aircraft map 52 where the decision supportmodel 58 has determined that the wearer of the mask 10 has likely beenunable to properly wear the mask 10 based on the sensor data 18, and hasoutput the recommended mask action 60 “ASSIST PASSENGER 7F WITH MASK.”Other potential recommended mask actions 60 may include dispatchingmedical attention, prioritizing certain passengers in an emergencylanding, resetting the sensors 16, changing altitude, resolvingpassenger mask issues on a certain side of the plane, and so on. In thismanner, the aircraft mask monitoring system 100 can assist the aircraftpersonnel in ensuring safety and directing resources where they are mostneeded.

The cabin crew view 42 of FIG. 4 can include some of the same GUIobjects as the flight deck view 40 and/or different objects. Forexample, the cabin crew view 42 can include an object 56N to indicatethat the issues for the selected masks 10 are resolved, the object 56Bto reset the sensors 16 of the selected mask(s) 10, an object 56O tonotify the flight deck of an issue with the oxygen source, an object 56Pto indicate that the cabin crew is ready to perform tasks related to theaircraft mask monitoring system 100, an object 56Q to request a completesensor reset by the flight deck or operations center, an object 56R toconfirm that the requested mask check has been completed, the object 56Hfor selecting which sensor data 18 to display in the data stream 44, andso on.

The one or more processors 24, 26 can be further configured to output anoperations center view 62 (see FIG. 5) on the client computing device 22in which the sensor data report 38 includes the data stream 44 of thesensor data 18 over a given time period. The operations center view 62can be intended to be used by the operations center staff which islocated remotely from the aircraft. The operations center view 62 caninclude many of the GUI objects displayed in the flight deck view 40. Insome implementations, the one or more processors 24, 26 can be furtherconfigured to send the flight deck or cabin crew of the aircraft analert based on the anomaly state. For example, the alert such as therecommended mask action 60 in FIG. 4 or any other suitable alert can besent to the flight deck or cabin crew automatically due to the executionof the decision support model 58, or may be sent after the user selectsa GUI object such as the object 56E, an object 56S for sending theflight deck a mask alert, the object 56G, the object 56A, the object56B, an object 56T for notifying the flight deck of an issue with anoxygen source 3, or an object 56U for notifying the flight deck or cabincrew to stand ready for passenger assistance or further instructions.

FIG. 6 shows a detailed view 64 of the operations center view 62 of FIG.5 by way of example. Other authorized users such as the maintenance crewor flight deck may also have permission to view a similar detailed view.The detailed view 64 can be used to view detailed statistical data,streaming sensor data 18, or other detailed data related to the aircraftmask monitoring system 100. One page 66 is illustrated by way ofexample, but additional pages of data may be accessible via tabs or menuoptions. In the example of FIG. 6, detailed sensor data is displayedwith ideal ranges/values in the bottom row for reference. Additionaldatapoints are viewable by scrolling horizontally or vertically.

In some implementations, the one or more processors 24, 26 can befurther configured to output stored historical sensor data 18 fordisplay in a maintenance view 68 on the client computing device 22, asshown in FIG. 7. As opposed to the views of FIGS. 4-6, the maintenanceview 68 is typically not used during an emergency event, but insteadduring a maintenance check of the aircraft 2 which may occur at regularintervals or be prompted due to an earlier event. GUI objects 70 can beused to view, sort, and select the stored historical sensor data 18, aswell as test the notification system. Additional objects 70 can be usedto access settings for auto compression, archiving, deleting,downloading, and viewing the stored historical sensor data 18. In someimplementations, the one or more processors 24, 26 can be furtherconfigured to reduce a sampling rate of the plurality of sensors 16, orfilter the sensor data 18 before storing, when collecting the sensordata 18 as the stored historical sensor data 18. This procedure may beautomatic, or may be implemented by, for example, the user selecting theobjects 70 to adjust the settings. Reducing the amount of data needingto be transferred, stored, and processed in this manner can save timeand storage costs.

An anomaly history viewer 72 can be provided for visualization after anevent in which an anomaly state is detected. In the depicted example,the anomaly history viewer 72 includes the chart 48 and a series ofgraphs 74 of the sensor data 18. A detailed data viewer 76 similar tothe detailed view 64 of FIG. 6 can be provided, and GUI objects 78 canbe used to override malfunctioning mask states, provide an applicationprogramming interface (API) for the client computing device 22 used bythe maintenance crew for in-depth reconfiguring of the aircraft maskmonitoring system 100, adjust or reset a clock of a mask 10 or of anoxygen source 3, configure the sensors 16 for a specific aircraftlayout, set thresholds for what constitutes an anomaly state, set datacapture intervals, view error logs, download error logs, or downloadsensor data 18. These GUI objects are merely an example and otherfunctionality may be provided in a suitable manner.

The one or more processors 24, 26 can be further configured to send thestored historical sensor data 18 for a period of time including aplurality of flights to the client computing device 22 during amaintenance inspection. Once received, the data can be analyzed in orderto further train any of the models used by the aircraft mask monitoringsystem 100. In some implementations, the stored historical sensor datacan provide a basis for a customized maintenance schedule. In this case,the one or more processors 24, 26 can be further configured to output arecommended maintenance action 80 based on the stored historical sensordata 18 and the anomaly state. For example, the recommended maintenanceaction 80 can include replacing a component of the aircraft maskmonitoring system 100 before a periodic component lifetime has elapsed,such as “REPLACE MASK 7F” because the aircraft mask monitoring system100 has determined that the particular mask 10 was faulty. In anotherexample, the recommended maintenance action 80 can include deferringreplacing a component of the aircraft mask monitoring system despite ascheduled lifetime elapsing, such as “DELAY OXYGEN SOURCE T1AREPLACEMENT 6 MONTHS” because the aircraft mask monitoring system 100has determined that the particular oxygen source 3 is in unusually goodcondition despite being at the end of its planned lifetime. That is, insome cases, the anomaly state may be a positive. In addition, theanomaly may occur during normal, non-emergency usage. For example, amask may deploy, partially deploy, or become unable to deploy, and therecommended maintenance action 80 can include replacing or repairing themask at the soonest opportunity. Aside from replacement, othermaintenance actions such as repairing, cleaning, etc., may be part ofthe customized maintenance schedule. In this manner, the aircraft maskmonitoring system 100 can avoid unnecessary costly maintenanceprocedures while ensuring safety.

FIG. 8 shows a method 800 of operating an aircraft mask monitoringsystem. The following description of method 800 is provided withreference to the system 100 comprising the plurality of masks 10respectively corresponding to a plurality of seats in the aircraft 2, asdescribed above and shown in FIG. 2. It will be appreciated that method800 may also be performed in other contexts using other suitablecomponents.

With reference to FIG. 8, at 802, the method 800 includes providing eachof the plurality of masks with at least one of a plurality of sensors.The plurality of sensors can include, for example, one or more of acarbon dioxide sensor, a mask state sensor, an oxygen flow sensor, atemperature sensor, a humidity sensor, an oxygen tank level sensor,and/or an oxygen generator sensor. At 804, the method 800 includesdetecting, as sensor data, at least one operational parameter of a maskfrom the plurality of masks. At 806, the method 800 includes receivingthe sensor data from the plurality of sensors at one or more processors.At 808, the method 800 includes storing the sensor data in an associatedstorage device. At 810, the method 800 includes processing the sensordata using anomaly detection logic to generate a sensor data report,wherein the sensor data report includes an anomaly state of at least onemask from the plurality of masks. At 812, the method 800 includesoutputting the sensor data report for display on a client computingdevice. In this manner, anomalies in the masks can be reported topersons in charge in situations of search and rescue, emergencylandings, medical emergencies, routine and emergent maintenance, and soon. As discussed above, the anomaly state can include at least one of anoffline state, an unknown state, a malfunction or depleted state of anassociated oxygen tank, a malfunction or depleted state of an oxygengenerator, an improperly deployed state, an abnormal breathing state ofa person wearing the mask, and/or a medical emergency state of theperson wearing the mask, among other states.

In some implementations, at 814, the method 800 includes executing adecision support model that is trained to receive the sensor data andoutput a recommended mask action in view of the anomaly state. Therecommended mask action can be output together with the sensor datareport for display on the client computing device. The sensor datareport can be outputted in a variety of views for use by variousparties, as illustrated at FIGS. 3-7. At 816, the method 800 includesoutputting a flight deck view or a cabin crew view on the clientcomputing device in which the sensor data report includes: a status ofeach of the plurality of masks, a data stream of the sensor data over agiven time period, and a metric indicating passenger health. In thismanner, the flight deck and crew can be presented with access to thesensor data report and any recommended mask action during a flight oremergency event. At 818, the method 800 includes outputting storedhistorical sensor data for display in a maintenance view on the clientcomputing device, and at 820, outputting a recommended maintenanceaction based on the stored historical sensor data and the anomaly state.In this manner, the maintenance can be improved using recommendationsfrom the aircraft mask monitoring system. For example, the recommendedmaintenance action can include replacing a component of the aircraftmask monitoring system before a scheduled lifetime has elapsed, or therecommended maintenance action can include deferring replacing acomponent of the aircraft mask monitoring system despite a scheduledlifetime elapsing. Finally, at 822, the method 800 includes outputtingan operations center view on the client computing device in which thesensor data report includes a data stream of the sensor data over agiven time period, and at 824, sending a flight deck or cabin crew ofthe aircraft an alert based on the anomaly state. In this manner, theaircraft mask monitoring system can assist the operations center inmanaging the personnel aboard the aircraft in the presence of safetyissues.

In some embodiments, the methods and processes described herein may betied to a computing system of one or more computing devices. Inparticular, such methods and processes may be implemented as acomputer-application program or service, an application-programminginterface (API), a library, and/or other computer-program product.

FIG. 9 schematically shows a non-limiting embodiment of a computingsystem 900 that can enact one or more of the methods and processesdescribed above. Computing system 900 is shown in simplified form.Computing system 900 may embody the aircraft mask monitoring system 100described above and illustrated in FIG. 2. Computing system 900 may takethe form of one or more personal computers, server computers, tabletcomputers, home-entertainment computers, network computing devices,gaming devices, mobile computing devices, mobile communication devices(e.g., smart phone), and/or other computing devices, and wearablecomputing devices such as smart wristwatches and head mounted augmentedreality devices.

Computing system 900 includes a logic processor 902 volatile memory 904,and a non-volatile storage device 906. Computing system 900 mayoptionally include a display subsystem 908, input subsystem 910,communication subsystem 912, and/or other components not shown in FIG.9.

Logic processor 902 includes one or more physical devices configured toexecute instructions. For example, the logic processor may be configuredto execute instructions that are part of one or more applications,programs, routines, libraries, objects, components, data structures, orother logical constructs. Such instructions may be implemented toperform a task, implement a data type, transform the state of one ormore components, achieve a technical effect, or otherwise arrive at adesired result.

The logic processor may include one or more physical processors(hardware) configured to execute software instructions. Additionally oralternatively, the logic processor may include one or more hardwarelogic circuits or firmware devices configured to executehardware-implemented logic or firmware instructions. Processors of thelogic processor 902 may be single-core or multi-core, and theinstructions executed thereon may be configured for sequential,parallel, and/or distributed processing. Individual components of thelogic processor optionally may be distributed among two or more separatedevices, which may be remotely located and/or configured for coordinatedprocessing. Aspects of the logic processor may be virtualized andexecuted by remotely accessible, networked computing devices configuredin a cloud-computing configuration. In such a case, these virtualizedaspects are run on different physical logic processors of variousdifferent machines, it will be understood.

Non-volatile storage device 906 includes one or more physical devicesconfigured to hold instructions executable by the logic processors toimplement the methods and processes described herein. When such methodsand processes are implemented, the state of non-volatile storage device906 may be transformed—e.g., to hold different data.

Non-volatile storage device 906 may include physical devices that areremovable and/or built-in. Non-volatile storage device 906 may includeoptical memory (e.g., CD, DVD, HD-DVD, Blu-Ray Disc, etc.),semiconductor memory (e.g., ROM, EPROM, EEPROM, FLASH memory, etc.),and/or magnetic memory (e.g., hard-disk drive, floppy-disk drive, tapedrive, MRAM, etc.), or other mass storage device technology.Non-volatile storage device 906 may include nonvolatile, dynamic,static, read/write, read-only, sequential-access, location-addressable,file-addressable, and/or content-addressable devices. It will beappreciated that non-volatile storage device 906 is configured to holdinstructions even when power is cut to the non-volatile storage device906.

Volatile memory 904 may include physical devices that include randomaccess memory. Volatile memory 904 is typically utilized by logicprocessor 902 to temporarily store information during processing ofsoftware instructions. It will be appreciated that volatile memory 904typically does not continue to store instructions when power is cut tothe volatile memory 904.

Aspects of logic processor 902, volatile memory 904, and non-volatilestorage device 906 may be integrated together into one or morehardware-logic components. Such hardware-logic components may includefield-programmable gate arrays (FPGAs), program- andapplication-specific integrated circuits (PASIC/ASICs), program- andapplication-specific standard products (PSSP/ASSPs), system-on-a-chip(SOC), and complex programmable logic devices (CPLDs), for example.

The term “program” may be used to describe an aspect of computing system900 typically implemented in software by a processor to perform aparticular function using portions of volatile memory, which functioninvolves transformative processing that specially configures theprocessor to perform the function. Thus, a program may be instantiatedvia logic processor 902 executing instructions held by non-volatilestorage device 906, using portions of volatile memory 904. It will beunderstood that different programs may be instantiated from the sameapplication, service, code block, object, library, routine, API,function, etc. Likewise, the same program may be instantiated bydifferent applications, services, code blocks, objects, routines, APIs,functions, etc. The term “program” may encompass individual or groups ofexecutable files, data files, libraries, drivers, scripts, databaserecords, etc.

When included, display subsystem 908 may be used to present a visualrepresentation of data held by non-volatile storage device 906. Thevisual representation may take the form of a graphical user interface(GUI). As the herein described methods and processes change the dataheld by the non-volatile storage device, and thus transform the state ofthe non-volatile storage device, the state of display subsystem 908 maylikewise be transformed to visually represent changes in the underlyingdata. Display subsystem 908 may include one or more display devicesutilizing virtually any type of technology. Such display devices may becombined with logic processor 902, volatile memory 904, and/ornon-volatile storage device 906 in a shared enclosure, or such displaydevices may be peripheral display devices.

When included, input subsystem 910 may comprise or interface with one ormore user-input devices such as a keyboard, mouse, touch screen, or gamecontroller. In some embodiments, the input subsystem may comprise orinterface with selected natural user input (NUI) componentry. Suchcomponentry may be integrated or peripheral, and the transduction and/orprocessing of input actions may be handled on- or off-board. Example NUIcomponentry may include a microphone for speech and/or voicerecognition; an infrared, color, stereoscopic, and/or depth camera formachine vision and/or gesture recognition; a head tracker, eye tracker,accelerometer, and/or gyroscope for motion detection and/or intentrecognition; as well as electric-field sensing componentry for assessingbrain activity; and/or any other suitable sensor.

When included, communication subsystem 912 may be configured tocommunicatively couple various computing devices described herein witheach other, and with other devices. Communication subsystem 912 mayinclude wired and/or wireless communication devices compatible with oneor more different communication protocols. As non-limiting examples, thecommunication subsystem may be configured for communication via awireless telephone network, or a wired or wireless local- or wide-areanetwork, such as a HDMI over Wi-Fi connection. In some embodiments, thecommunication subsystem may allow computing system 900 to send and/orreceive messages to and/or from other devices via a network such as theInternet.

The following paragraphs provide additional support for the claims ofthe subject application. One aspect provides an aircraft mask monitoringsystem. The aircraft mask monitoring system comprises a plurality ofmasks respectively corresponding to a plurality of seats in an aircraft,wherein the masks are configured to deliver oxygen, a plurality ofsensors, wherein each of the plurality of masks includes at least one ofthe plurality of sensors, and wherein each sensor is configured todetect an operational parameter of the respective mask as sensor data,and one or more processors. The one or more processors are configured tocommunicate with the plurality of sensors via a wired or wirelessconnection, receive the sensor data from the plurality of sensors, storethe sensor data in an associated storage device, process the sensor datausing anomaly detection logic to generate a sensor data report, whereinthe sensor data report includes an anomaly state of at least one maskfrom the plurality of masks, and output the sensor data report fordisplay on a client computing device. In this aspect, additionally oralternatively, the one or more processors are further configured toexecute a decision support model that is trained to receive the sensordata and output a recommended mask action in view of the anomaly state.In this aspect, additionally or alternatively, the anomaly stateincludes at least one of an offline state, an unknown state, amalfunction or depleted state of an associated oxygen tank, amalfunction or depleted state of an oxygen generator, an improperlydeployed state, an abnormal breathing state of a person wearing themask, and/or a medical emergency state of the person wearing the mask.In this aspect, additionally or alternatively, the one or moreprocessors are further configured to output stored historical sensordata for display in a maintenance view on the client computing device,and output a recommended maintenance action based on the storedhistorical sensor data and the anomaly state. In this aspect,additionally or alternatively, the recommended maintenance actionincludes replacing a component of the aircraft mask monitoring systembefore a periodic component lifetime has elapsed. In this aspect,additionally or alternatively, the recommended maintenance actionincludes deferring replacing a component of the aircraft mask monitoringsystem despite a scheduled lifetime elapsing. In this aspect,additionally or alternatively, the one or more processors are furtherconfigured to reduce a sampling rate of the plurality of sensors, orfilter the sensor data before storing, when collecting the sensor dataas the stored historical sensor data, and send the stored historicalsensor data for a period of time, including a plurality of flights, tothe client device during a maintenance inspection. In this aspect,additionally or alternatively, the one or more processors are furtherconfigured to output a flight deck view or a cabin crew view on theclient computing device in which the sensor data report includes astatus of each of the plurality of masks, a data stream of the sensordata over a given time period, and a metric indicating passenger health.In this aspect, additionally or alternatively, the one or moreprocessors are further configured to output an operations center view onthe client computing device in which the sensor data report includes adata stream of the sensor data over a given time period, and send aflight deck or cabin crew of the aircraft an alert based on the anomalystate. In this aspect, additionally or alternatively, the plurality ofsensors include one or more of a carbon dioxide sensor, a mask statesensor, an oxygen flow sensor, a temperature sensor, a humidity sensor,an oxygen tank level sensor, and/or an oxygen generator sensor.

Another aspect provides a method of operating an aircraft maskmonitoring system, the system comprising a plurality of masksrespectively corresponding to a plurality of seats in an aircraft, themasks configured to deliver oxygen. The method comprises providing eachof the plurality of masks with at least one of a plurality of sensors,detecting, as sensor data, at least one operational parameter of a maskfrom the plurality of masks, receiving the sensor data from theplurality of sensors at one or more processors, storing the sensor datain an associated storage device, processing the sensor data usinganomaly detection logic to generate a sensor data report, wherein thesensor data report includes an anomaly state of at least one mask fromthe plurality of masks, and outputting the sensor data report fordisplay on a client computing device. In this aspect, additionally oralternatively, the method further comprises executing a decision supportmodel that is trained to receive the sensor data and output arecommended mask action in view of the anomaly state. In this aspect,additionally or alternatively, the anomaly state includes at least oneof an offline state, an unknown state, a malfunction or depleted stateof an associated oxygen tank, a malfunction or depleted state of anoxygen generator, an improperly deployed state, an abnormal breathingstate of a person wearing the mask, and/or a medical emergency state ofthe person wearing the mask. In this aspect, additionally oralternatively, the method further comprises outputting stored historicalsensor data for display in a maintenance view on the client computingdevice, and outputting a recommended maintenance action based on thestored historical sensor data and the anomaly state. In this aspect,additionally or alternatively, the recommended maintenance actionincludes replacing a component of the aircraft mask monitoring systembefore a scheduled lifetime has elapsed. In this aspect, additionally oralternatively, the recommended maintenance action includes deferringreplacing a component of the aircraft mask monitoring system despite ascheduled lifetime elapsing. In this aspect, additionally oralternatively, the method further comprises outputting a flight deckview or a cabin crew view on the client computing device in which thesensor data report includes a status of each of the plurality of masks,a data stream of the sensor data over a given time period, and a metricindicating passenger health. In this aspect, additionally oralternatively, the method further comprises outputting an operationscenter view on the client computing device in which the sensor datareport includes a data stream of the sensor data over a given timeperiod, and sending a flight deck or cabin crew of the aircraft an alertbased on the anomaly state. In this aspect, additionally oralternatively, the plurality of sensors include one or more of a carbondioxide sensor, a mask state sensor, an oxygen flow sensor, atemperature sensor, a humidity sensor, an oxygen tank level sensor,and/or an oxygen generator sensor.

Another aspect provides a mask for oxygen delivery in an aircraft. Themask comprises a mask body configured to cover a nose and mouth of apassenger, a bag connected to the mask body and to an oxygen source, anda plurality of sensors configured to detect a plurality of operationalparameters of the mask as sensor data. The plurality of sensors includea carbon dioxide sensor, a mask state sensor, an oxygen flow sensor, atemperature sensor, and a humidity sensor, and the plurality of sensorsare configured to communicate the sensor data to one or more processorsvia a wired or wireless connection.

“And/or” as used herein is defined as the inclusive or V, as specifiedby the following truth table:

A B A ∨ B True True True True False True False True True False FalseFalse

It will be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofstrategies. As such, various acts illustrated and/or described may beperformed in the sequence illustrated and/or described, in othersequences, in parallel, or omitted. Likewise, the order of theabove-described processes may be changed.

The subject matter of the present disclosure includes all novel andnon-obvious combinations and sub-combinations of the various processes,systems, and configurations, and other features, functions, acts, and/orproperties.

1. An aircraft mask monitoring system, comprising: a plurality of masksrespectively corresponding to a plurality of seats in an aircraft,wherein the masks are configured to deliver oxygen; a plurality ofsensors, wherein each of the plurality of masks includes at least one ofthe plurality of sensors, and wherein each sensor is configured todetect an operational parameter of the respective mask as sensor data;and one or more processors configured to: communicate with the pluralityof sensors via a wired or wireless connection; receive the sensor datafrom the plurality of sensors; store the sensor data in an associatedstorage device; process the sensor data using anomaly detection logic togenerate a sensor data report, wherein the sensor data report includesan anomaly state of at least one mask from the plurality of masks; andoutput the sensor data report for display on a client computing device.2. The aircraft mask monitoring system of claim 1, wherein the one ormore processors are further configured to execute a decision supportmodel that is trained to receive the sensor data and output arecommended mask action in view of the anomaly state.
 3. The aircraftmask monitoring system of claim 1, wherein the anomaly state includes atleast one of an offline state, an unknown state, a malfunction ordepleted state of an associated oxygen tank, a malfunction or depletedstate of an oxygen generator, an improperly deployed state, an abnormalbreathing state of a person wearing the mask, and/or a medical emergencystate of the person wearing the mask.
 4. The aircraft mask monitoringsystem of claim 1, wherein the one or more processors are furtherconfigured to: output stored historical sensor data for display in amaintenance view on the client computing device; and output arecommended maintenance action based on the stored historical sensordata and the anomaly state.
 5. The aircraft mask monitoring system ofclaim 4, wherein the recommended maintenance action includes replacing acomponent of the aircraft mask monitoring system before a periodiccomponent lifetime has elapsed.
 6. The aircraft mask monitoring systemof claim 4, wherein the recommended maintenance action includesdeferring replacing a component of the aircraft mask monitoring systemdespite a scheduled lifetime elapsing.
 7. The aircraft mask monitoringsystem of claim 4, wherein the one or more processors are furtherconfigured to: reduce a sampling rate of the plurality of sensors orfilter the sensor data before storing, when collecting the sensor dataas the stored historical sensor data; and send the stored historicalsensor data for a period of time, including a plurality of flights, tothe client device during a maintenance inspection.
 8. The aircraft maskmonitoring system of claim 1, wherein the one or more processors arefurther configured to output a flight deck view or a cabin crew view onthe client computing device in which the sensor data report includes: astatus of each of the plurality of masks; a data stream of the sensordata over a given time period; and a metric indicating passenger health.9. The aircraft mask monitoring system of claim 1, wherein the one ormore processors are further configured to: output an operations centerview on the client computing device in which the sensor data reportincludes a data stream of the sensor data over a given time period; andsend a flight deck or cabin crew of the aircraft an alert based on theanomaly state.
 10. The aircraft mask monitoring system of claim 1,wherein the plurality of sensors include one or more of a carbon dioxidesensor, a mask state sensor, an oxygen flow sensor, a temperaturesensor, a humidity sensor, an oxygen tank level sensor, and/or an oxygengenerator sensor.
 11. A method of operating an aircraft mask monitoringsystem, the system comprising a plurality of masks respectivelycorresponding to a plurality of seats in an aircraft, the masksconfigured to deliver oxygen, the method comprising: providing each ofthe plurality of masks with at least one of a plurality of sensors;detecting, as sensor data, at least one operational parameter of a maskfrom the plurality of masks; receiving the sensor data from theplurality of sensors at one or more processors; storing the sensor datain an associated storage device; processing the sensor data usinganomaly detection logic to generate a sensor data report, wherein thesensor data report includes an anomaly state of at least one mask fromthe plurality of masks; and outputting the sensor data report fordisplay on a client computing device.
 12. The method of claim 11,further comprising executing a decision support model that is trained toreceive the sensor data and output a recommended mask action in view ofthe anomaly state.
 13. The method of claim 11, wherein the anomaly stateincludes at least one of an offline state, an unknown state, amalfunction or depleted state of an associated oxygen tank, amalfunction or depleted state of an oxygen generator, an improperlydeployed state, an abnormal breathing state of a person wearing themask, and/or a medical emergency state of the person wearing the mask.14. The method of claim 11, further comprising: outputting storedhistorical sensor data for display in a maintenance view on the clientcomputing device; and outputting a recommended maintenance action basedon the stored historical sensor data and the anomaly state.
 15. Themethod of claim 14, wherein the recommended maintenance action includesreplacing a component of the aircraft mask monitoring system before ascheduled lifetime has elapsed.
 16. The method of claim 14, wherein therecommended maintenance action includes deferring replacing a componentof the aircraft mask monitoring system despite a scheduled lifetimeelapsing.
 17. The method of claim 11, further comprising outputting aflight deck view or a cabin crew view on the client computing device inwhich the sensor data report includes: a status of each of the pluralityof masks; a data stream of the sensor data over a given time period; anda metric indicating passenger health.
 18. The method of claim 11,further comprising: outputting an operations center view on the clientcomputing device in which the sensor data report includes a data streamof the sensor data over a given time period; and sending a flight deckor cabin crew of the aircraft an alert based on the anomaly state. 19.The method of claim 11, wherein the plurality of sensors include one ormore of a carbon dioxide sensor, a mask state sensor, an oxygen flowsensor, a temperature sensor, a humidity sensor, an oxygen tank levelsensor, and/or an oxygen generator sensor.
 20. A mask for oxygendelivery in an aircraft, the mask comprising: a mask body configured tocover a nose and mouth of a passenger; a bag connected to the mask bodyand to an oxygen source; and a plurality of sensors configured to detecta plurality of operational parameters of the mask as sensor data,wherein: the plurality of sensors include a carbon dioxide sensor, amask state sensor, an oxygen flow sensor, a temperature sensor, and ahumidity sensor; and the plurality of sensors are configured tocommunicate the sensor data to one or more processors via a wired orwireless connection.